Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill ara-rigor-reviewgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ara-rigor-review)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ara-rigor-review"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ara-rigor-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ara-rigor-review"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ara-rigor-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00600 |
| Opus 5 | $0.00021 | $0.00300 |
| Sonnet 5 | $0.00008 | $0.00120 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
ara-rigor-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
SOP: ARA Rigor Review
Key question: 这份 ARA 的认识论严谨度如何?逻辑弧在结构上闭合了吗?
Preflight
先确认外部 rigor-reviewer skill 可 load。不可用则提示安装并停下。
Procedure
-
跑 Level 2:
Skillload rigor-reviewer,传<artifact_dir>=../ara/。 它对 ARA 跑六维语义审查(全是要读懂 + 推理的语义检查,不是结构校验):- D1 Evidence Relevance — 证据是否在实质上支撑每条 claim;
- D2 Falsifiability Quality — 证伪标准是否有意义、可操作、范围合适;
- D3 Scope Calibration — claim 是否恰好断言其证据所支撑的,不多不少;
- D4 Argument Coherence — 是否从 problem→solution→evidence 逻辑闭合;
- D5 Exploration Integrity — exploration tree 是否记录了真实研究过程(含失败);
- D6 Methodological Rigor — 实验设计/baseline/ablation/报告是否到位。
-
产物:
rigor-reviewer在 artifact 根目录写level2_report.json(每维 1–5 分 + strengths/weaknesses/suggestions + severity 排序 findings + overall grade + 给作者的问题)。 -
D5 低分不是错误,是"探索素材不足"信号。 透传给用户,由用户决定是否回
context-exploring补打捞过程线。本 SOP 不自动循环。
注意:
rigor-reviewer的 D1–D6 是 ARA 自己的维度,与 DARE 的 D1–D5 评判 标准是两套东西,不要混。本 SOP 只透传 ARA 的报告,不施加 DARE 的 D1–D5。
Output
ara/level2_report.json + 一句话总结(grade + 最该关注的 finding),交付用户。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 47 lines · 42 tokens per session scan A ee6822832a6d
ara-rigor-review is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 2d ago), licensed Apache-2.0. It adds 42 tokens to every session and 600 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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